[SERVER] Added documentation for parallel_tool_calls param

common/chat.cpp

@@ -1758,6 +1758,13 @@ static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat
 
     data.prompt = apply(tmpl, inputs, /* messages_override =*/ std::nullopt, /* tools_override= */ std::nullopt, extra_context);
     data.format = COMMON_CHAT_FORMAT_HERMES_2_PRO;
+    auto supports_thinking = tmpl.source().find("<think>") != std::string::npos;
+
+    // you should not be able to call enable_thinking if <think> is not supported
+    if (!supports_thinking && extra_context["enable_thinking"]) {
+        extra_context["enable_thinking"] = false;
+    }
+
     if (string_ends_with(data.prompt, "<think>\n")) {
         if (!extra_context["enable_thinking"]) {
             data.prompt += "</think>";
@@ -1820,9 +1827,31 @@ static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat
             tool_call_alts.push_back(
                 "( \"```\\n\" | \"```json\\n\" | \"```xml\\n\" ) space " + wrappable_tool_call + " space \"```\" space ");
             auto tool_call = builder.add_rule("tool_call", string_join(tool_call_alts, " | "));
-            builder.add_rule("root",
-                std::string(data.thinking_forced_open ? "( \"</think>\" space )? " : "") +
-                (inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call));
+
+            builder.add_rule("thinking-start", "\"<think>\"");
+            builder.add_rule("thinking-content", "( [^<] | \"<\" [^/] | \"</\" [^t] | \"</t\" [^h] | \"</th\" [^i] | \"</thi\" [^n] | \"</thin\" [^k] | \"</think\" [^>] )*");
+            builder.add_rule("thinking-end", "\"</think>\" space");
+
+            //thinking grammar logic depending on if thinking_forced_open was to true (so already opened (and maybe closed)) and if thinking is even allowed
+            std::string thinking_grammar_logic = ""; // thinking tag was closed or not supported/wanted
+            if (extra_context["enable_thinking"]) {
+                data.grammar_triggers.push_back({
+                    COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
+                    data.thinking_forced_open ? "</think>" : "<think>"
+                });
+                if (data.thinking_forced_open) {
+                    //thinking tag was already opened by used so we don't need to add it again
+                    thinking_grammar_logic = "(thinking-content thinking-end) ";
+                }
+                else
+                {
+                    thinking_grammar_logic = "(thinking-start thinking-content thinking-end) ";
+                }
+            }
+
+
+            builder.add_rule("root", thinking_grammar_logic + (inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call));
+
             // Trigger on some common known "good bad" outputs (only from the start and with a json that's about a specific argument name to avoid false positives)
             data.grammar_triggers.push_back({
                 COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,

docs/function-calling.md

@@ -21,6 +21,8 @@ Function calling is supported for all models (see https://github.com/ggml-org/ll
   - Use `--chat-template-file` to override the template when appropriate (see examples below)
   - Generic support may consume more tokens and be less efficient than a model's native format.
 
+- Multiple/parallel tool calling is supported on some models but disabled by default, enable it by passing `"parallel_tool_calls": true` in the completion endpoint payload.
+
 <details>
 <summary>Show some common templates and which format handler they use</summary>
 

ggml/src/ggml-cpu/ggml-cpu-impl.h

@@ -489,7 +489,7 @@ inline static int16x8_t vec_padd_s16(int16x8_t a, int16x8_t b) {
 /**
  * @see https://github.com/ggml-org/llama.cpp/pull/14037
  */
-inline float vec_hsum(float32x4_t v) {
+inline static float vec_hsum(float32x4_t v) {
     float32x4_t v_temp = v + vec_reve(v);
     return v_temp[0] + v_temp[1];
 }

ggml/src/ggml-cuda/conv2d.cu

@@ -0,0 +1,171 @@
+#include "conv2d.cuh"
+
+struct conv_params {
+    const int64_t IW, IH;
+    const int64_t OW, OH;
+    const int64_t KW, KH;
+    const int64_t ST_X, ST_Y;
+    const int64_t PD_X, PD_Y;
+    const int64_t DL_X, DL_Y;
+    const int64_t IC, OC;
+    const int64_t B;
+    const int64_t TOTAL;
+};
+
+struct kernel_bounds {
+    int64_t y_min, y_max;
+    int64_t x_min, x_max;
+};
+
+__device__ __forceinline__ int64_t max64(int64_t a, int64_t b) {
+    return (a > b) ? a : b;
+}
+
+__device__ __forceinline__ int64_t min64(int64_t a, int64_t b) {
+    return (a < b) ? a : b;
+}
+
+__device__ __forceinline__ kernel_bounds calculate_kernel_bounds(int64_t out_x, int64_t out_y, const conv_params & P) {
+    kernel_bounds bounds;
+    bounds.y_min = max64(0, (P.PD_Y - out_y * P.ST_Y + P.DL_Y - 1) / P.DL_Y);
+    bounds.y_max = min64(P.KH, (P.IH + P.PD_Y - out_y * P.ST_Y + P.DL_Y - 1) / P.DL_Y);
+    bounds.x_min = max64(0, (P.PD_X - out_x * P.ST_X + P.DL_X - 1) / P.DL_X);
+    bounds.x_max = min64(P.KW, (P.IW + P.PD_X - out_x * P.ST_X + P.DL_X - 1) / P.DL_X);
+    return bounds;
+}
+
+__device__ __forceinline__ int calculate_input_coord(int64_t out_coord,
+                                                     int64_t kern_coord,
+                                                     int64_t stride,
+                                                     int64_t dilation,
+                                                     int64_t padding) {
+    return out_coord * stride + kern_coord * dilation - padding;
+}
+
+struct whcn_layout {
+    __device__ static int64_t input_index(int64_t n, int64_t c, int64_t y, int64_t x, const conv_params & P) {
+        return n * (P.IC * P.IW * P.IH) + c * P.IW * P.IH + y * P.IW + x;
+    }
+
+    __device__ static int64_t kernel_index(int64_t c_out, int64_t c_in, int64_t ky, int64_t kx, const conv_params & P) {
+        return c_out * (P.IC * P.KH * P.KW) + c_in * (P.KH * P.KW) + ky * P.KW + kx;
+    }
+
+    __device__ static int64_t output_index(int64_t n, int64_t c, int64_t y, int64_t x, const conv_params & P) {
+        return n * (P.OC * P.OW * P.OH) + c * P.OW * P.OH + y * P.OW + x;
+    }
+
+    __device__ static void unpack_indices(int64_t             global_idx,
+                                          const conv_params & P,
+                                          int64_t &           n,
+                                          int64_t &           c,
+                                          int64_t &           out_y,
+                                          int64_t &           out_x) {
+        out_x = global_idx % P.OW;
+        out_y = (global_idx / P.OW) % P.OH;
+        c     = (global_idx / (P.OW * P.OH)) % P.OC;
+        n     = global_idx / (P.OW * P.OH * P.OC);
+    }
+};
+
+template <typename T, typename Layout>
+static __global__ void conv2d_kernel(const float * __restrict__ input,
+                                     const T * __restrict__ kernel,
+                                     float * __restrict__ output,
+                                     const conv_params P) {
+    const int64_t global_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (global_idx >= P.TOTAL) {
+        return;
+    }
+
+    int64_t n, c_out, out_y, out_x;
+    Layout::unpack_indices(global_idx, P, n, c_out, out_y, out_x);
+
+    T acc = 0;
+
+    for (int64_t c_in = 0; c_in < P.IC; ++c_in) {
+        kernel_bounds bounds = calculate_kernel_bounds(out_x, out_y, P);
+
+        for (int64_t ky = bounds.y_min; ky < bounds.y_max; ++ky) {
+            const int64_t in_y = calculate_input_coord(out_y, ky, P.ST_Y, P.DL_Y, P.PD_Y);
+
+            for (int64_t kx = bounds.x_min; kx < bounds.x_max; ++kx) {
+                const int64_t in_x = calculate_input_coord(out_x, kx, P.ST_X, P.DL_X, P.PD_X);
+
+                T input_val;
+                if (std::is_same<T, half>::value) {
+                    input_val = __float2half(input[Layout::input_index(n, c_in, in_y, in_x, P)]);
+                } else {
+                    input_val = input[Layout::input_index(n, c_in, in_y, in_x, P)];
+                }
+
+                T kernel_val = kernel[Layout::kernel_index(c_out, c_in, ky, kx, P)];
+                acc += (input_val * kernel_val);
+            }
+        }
+    }
+
+    // [N, OC, OH, OW]
+    output[Layout::output_index(n, c_out, out_y, out_x, P)] = (float) acc;
+}
+
+template <typename T>
+static void conv2d_cuda(const float * X_D, const T * K_D, float * Y_D, const conv_params P, cudaStream_t st) {
+    const int blocks = (P.TOTAL + CUDA_CONV2D_BLOCK_SIZE - 1) / CUDA_CONV2D_BLOCK_SIZE;
+    conv2d_kernel<T, whcn_layout><<<blocks, CUDA_CONV2D_BLOCK_SIZE, 0, st>>>(X_D, K_D, Y_D, P);
+}
+
+static void conv2d_cuda_f16(const float * X_D, const half * K_D, float * Y_D, const conv_params P, cudaStream_t st) {
+    conv2d_cuda<half>(X_D, K_D, Y_D, P, st);
+}
+
+static void conv2d_cuda_f32(const float * X_D, const float * K_D, float * Y_D, const conv_params P, cudaStream_t st) {
+    conv2d_cuda<float>(X_D, K_D, Y_D, P, st);
+}
+
+void ggml_cuda_op_conv2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * kernel = dst->src[0];
+    const ggml_tensor * input  = dst->src[1];
+    float *             K_D    = (float *) kernel->data;
+    const float *       X_D    = (const float *) input->data;
+    float *             Y_D    = (float *) dst->data;
+
+    GGML_ASSERT(ggml_is_contiguous(kernel));
+    GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32);
+
+    // same number of input channels
+    GGML_ASSERT(input->ne[2] == kernel->ne[2]);
+
+    cudaStream_t st = ctx.stream();
+
+    const int32_t * p    = (const int32_t *) dst->op_params;
+    const int       ST_X = p[0];  // stride_x
+    const int       ST_Y = p[1];  // stride_y
+    const int       PD_X = p[2];  // padding_x
+    const int       PD_Y = p[3];  // padding_y
+    const int       DL_X = p[4];  // dilation_x
+    const int       DL_Y = p[5];  // dilation_y
+
+    // No cwhn
+    GGML_ASSERT(p[6] == false);
+
+    const int IW = input->ne[0];   // input_w
+    const int IH = input->ne[1];   // input_h
+    const int OW = dst->ne[0];     // output_w
+    const int OH = dst->ne[1];     // output_h
+    const int KW = kernel->ne[0];  // kernel_w
+    const int KH = kernel->ne[1];  // kernel_h
+    const int IC = input->ne[2];   // input_channels
+    const int OC = kernel->ne[3];  // ouptut_chanles
+    const int B  = input->ne[3];   // n_batches
+
+    const int64_t total  = B * OC * OH * OW;
+    conv_params   params = { IW, IH, OW, OH, KW, KH, ST_X, ST_Y, PD_X, PD_Y, DL_X, DL_Y, IC, OC, B, total };
+
+    if (kernel->type == GGML_TYPE_F16) {
+        conv2d_cuda_f16(X_D, (half *) K_D, Y_D, params, st);
+    } else {
+        conv2d_cuda_f32(X_D, K_D, Y_D, params, st);
+    }
+}

ggml/src/ggml-cuda/conv2d.cuh

@@ -0,0 +1,5 @@
+#pragma once
+#include "common.cuh"
+
+#define CUDA_CONV2D_BLOCK_SIZE 256
+void ggml_cuda_op_conv2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -12,6 +12,7 @@
 #include "ggml-cuda/clamp.cuh"
 #include "ggml-cuda/concat.cuh"
 #include "ggml-cuda/conv-transpose-1d.cuh"
+#include "ggml-cuda/conv2d.cuh"
 #include "ggml-cuda/conv2d-dw.cuh"
 #include "ggml-cuda/conv2d-transpose.cuh"
 #include "ggml-cuda/convert.cuh"
@@ -2451,6 +2452,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_IM2COL:
             ggml_cuda_op_im2col(ctx, dst);
             break;
+        case GGML_OP_CONV_2D:
+            ggml_cuda_op_conv2d(ctx, dst);
+            break;
         case GGML_OP_CONV_2D_DW:
             ggml_cuda_op_conv2d_dw(ctx, dst);
             break;
@@ -3501,6 +3505,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
             return op->src[0]->nb[0] == ggml_type_size(op->src[0]->type) && ggml_is_contiguous_2(op->src[0]);
         }
         case GGML_OP_IM2COL:
+        case GGML_OP_CONV_2D:
         case GGML_OP_CONV_2D_DW:
         case GGML_OP_CONV_TRANSPOSE_2D:
         case GGML_OP_POOL_2D:

tools/server/README.md

@@ -1143,6 +1143,8 @@ The `response_format` parameter supports both plain JSON output (e.g. `{"type":
 
 `parse_tool_calls`: Whether to parse the generated tool call.
 
+`parallel_tool_calls` : Whether to enable parallel/multiple tool calls (only supported on some models, verification is based on jinja template).
+
 *Examples:*
 
 You can use either Python `openai` library with appropriate checkpoints: